Application of human Schwann cells derived from skin neural precursor cells to the engineering of the peripheral nervous system
Bibliographic record
Abstract
The isolation of autologous neural precursors from human skin‐derived precursor cells would be a very efficient source of Schwann cells for the treatment of various disorders of the nervous system and also for the development of tissue engineered models to better understand the pathogenesis of neurodegenerative diseases. The purpose of this study was to demonstrate that these neural precursors were able to differentiate into mature Schwann cells and to use them in the engineering of models of the nervous system. We isolated neural precursors from human breast skin and expanded them in vitro. Once cultured in differentiation medium, they have the typical Schwann cells's bipolar morphology. We have shown by indirect immunofluorescent staining and Western Blot that these cells expressed the Schwann cell markers p75NTR, S100B and CNPase. We then reconstructed tissue‐engineered connective tissues enriched with differentiated human Schwann cells. We conclude that the generation of autologous Schwann cells from an accessible adult human source opens many potential therapeutic applications and has a great interest for experimental studies on the normal human Schwann cell physiology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".